Automatic analysis in virtual endoscopy

ABSTRACT

A computer system and a computer-implemented method are provided for interactively displaying a three-dimensional rendering of a structure having a lumen and for indicating regions of abnormal wall structure. A three-dimensional volume of data is formed from a series of two-dimensional images representing at least one physical property associated with the three-dimensional structure. An isosurface of a selected region of interest is created by a computer from the volume of data based on a selected value or values of a physical property representing the selected region of interest. A wireframe model of the isosurface is generated by the computer wherein the wireframe model includes a plurality of vertices. The vertices are then grouped into populations of contiguous vertices having a characteristic indicating abnormal wall structure by the computer. The wireframe model is then rendered by the computer in an interactive three-dimensional display to indicate the populations of abnormal wall structure.

RELATED APPLICATION

This application is a continuation of co-pending application Ser. No.10/109,547, entitled, “Automatic Analysis in Virtual Endoscopy”, filedon Mar. 28, 2002, now issued as U.S. Pat. No. 7,149,564, which in turnis a continuation of application Ser. No. 09/299,061, entitled“Automatic Analysis in Virtual Endoscopy”, filed on Apr. 23, 1999, nowissued as U.S. Pat. No. 6,366,800, which in turn is a continuation ofapplication Ser. No. 08/805,584, entitled “Automatic Analysis in VirtualEndoscopy”, filed on Feb. 25, 1997, now issued as U.S. Pat. No.5,920,319, which in turn is a continuation-in-part of application Ser.No. 08/331,352, filed on Oct. 27, 1994, now issued as U.S. Pat. No.5,782,762, which are each incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to a system and method for interactivelydisplaying a three-dimensional rendering of a structure having a lumenand, more particularly, to a system and method for automaticallyanalyzing such structures for potential abnormalities.

BACKGROUND OF THE INVENTION

For many forms of cancer, early detection is essential for a favorableprognosis. A cancerous growth must be detected at an early stage beforethe cancer is allowed to grow and spread. This is particularly true forcolorectal and lung cancers. As a result, endoscopic techniques havebeen developed to examine the colon and tracheobronchial airways for thegrowth of precancerous and cancerous masses.

Regarding colorectal cancer, the American Cancer Society and theNational Cancer Institute recommend routine screening beginning at age55 in order to provide a means for early detection of abnormal growths.Under those guidelines over 50 million Americans should undergo annualcolorectal screening. However, only about 1.5 million fiberopticcolonoscopies are performed each year. The discrepancy arises, at leastin part, because conventional colonoscopies require a patient to besedated so that a flexible endoscope can be inserted into the patient'sanus. Another problem is that conventional colonoscopies fail to provideaccess to the entire colon in approximately 15% of cases. Colonoscopiesalso expose patients to the risk of bowel perforation. Accordingly, itis understandable that patients are reluctant to undergo, or repeatedlysubject themselves to, such an invasive procedure.

Consequently, virtual endoscopic techniques have been, and arecontinuing to be developed. Virtual endoscopy that utilizes computerreformation of radiologic cross-sectional images is a minimally invasivealternative to conventional fiberoptic endoscopy. Virtual endoscopyreduces the risk of perforation, does not require any sedation, and isconsiderably less expensive than the fiberoptic endoscopy method. Forexample, virtual colonoscopy techniques generally require bowelcleansing, gas distension of the colon, a 40-60 second spiral computedtomography (CT) scan of a patient's abdomen and pelvis, and human visualanalysis of multi-planar two-dimensional (2D) and three-dimensional (3D)images created from CT data.

Although virtual colonoscopy techniques provide excellent images of thecolon in three-dimensions, a correct diagnosis hinges upon a physician'sability to properly identify small (approximately 5-10 mm), andsometimes subtle, masses within hundreds of multiplanar two-dimensionaland three-dimensional images. Such human inspection is time consuming,tedious, expensive, and under certain circumstances prone to error ofinterpretation. Accordingly, it would be highly beneficial to provide anautomatic virtual endoscopic system and method for automaticallyanalyzing and/or detecting abnormalities, such as abnormal growths, in atargeted structure or organ system, such as the walls of a colon ortrachea.

SUMMARY OF THE INVENTION

In accordance with the present invention, a computer-implemented methodand computer system are provided for interactively displaying athree-dimensional rendering of a structure having a lumen. In a specificapplication, the method may be utilized for analyzing regions havingcertain characteristics of wall structure such as thickness or shape todetect, for example, abnormal masses. In accordance with the method ofthe present invention, a three-dimensional volume of data is formed froma series of two-dimensional images representing at least one physicalproperty associated with the three-dimensional object. An isosurface ofa selected region of interest is created in the computer from the volumeof data based on selected values of the physical properties representingthe selected region of interest. A wireframe model of the isosurface isgenerated by the computer. The wireframe model is analyzed to detect, bycomputer, those sections of the object having the selectedcharacteristic such as abnormal wall structure. For example, thewireframe model includes a plurality of vertices. Thecomputer-implemented method groups the vertices of the wireframe modelinto populations having a characteristic indicating abnormal wallstructure. The wireframe model with highlighted abnormal portions isthen rendered by the computer into an interactive three-dimensionaldisplay on the computer monitor.

In specific applications, the step of grouping the vertices intopopulations having a characteristic indicating abnormal wall structureis effected by determining a normal vertex for each vertex position ofthe wireframe model. Next, a connectivity matrix is determined toprovide sets of contiguous vertices for each vertex. A wall thicknessvalue associated with each normal vector is determined for each vertex.Local convexity and curvature are also determined on a per vertex basis.Then, contiguous vertices having a characteristic indicating abnormalthicknesses or other abnormal properties are grouped together intoseparate populations indicative of areas of abnormality. Either as asupplement or an alternative to determining abnormal thicknesses, thecomputer-implemented method may function to analyze shapecharacteristics of selected populations of vertices. For example, eachpopulation of contiguous vertices may be determined based on acharacteristic such as convexity or curvature. Each population is thenanalyzed to determine a convexity value for each population representingan amount and direction of convexity of the population on the wireframemodel. In selected applications, each population may be analyzedaccording to other selected shape characteristics.

In accordance with the present invention, a method and system areprovided for interactively displaying a three-dimensional rendering of astructure having a lumen wherein the rendering is effected by a anadaptive thresholding procedure. A three-dimensional volume of data isformed in a computer from a series of two-dimensional images acquired,for example, by a scanner such as CT-scanner. The acquired imagesrepresent at least one physical property associated with thethree-dimensional object. A selected region of interest is segmented bythe computer from the volume of data based on a selected criteria suchas a threshold value of the physical property. The threshold value isadaptively adjusted in the region of interest to control the segmentingof the selected region of interest. An isosurface of a selected regionof interest is created by the computer from the volume of data based onselected values, such as the adaptive adjusted thresholds of thephysical property representing the selected region of interest. Next, awireframe model of the isosurface is generated and then rendered in aninteractive three-dimensional display.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe preferred embodiments of the present invention, will be betterunderstood when read in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a flowchart representing a method in accordance with thepresent invention of producing an interactive, three-dimensionalrendering of a selected organ system, such as a lumenous organ, and ofdetecting potentially abnormal areas of the organ system;

FIG. 2 is a flowchart representing a method for analyzing the shape of apopulation representing a potentially abnormal area within the selectedorgan system;

FIG. 3 is a flowchart representing a method for measuring the selectedorgan system's wall thickness;

FIG. 4 is a block diagram of a system used in the method of the presentinvention;

FIG. 5 is a schematic representation of a two-dimensional plane througha wireframe model of an area of abnormal wall structure illustrating amethod for determining the convexity of the area;

FIG. 6 is a flowchart representing a method for adaptively adjusting athreshold value during the segmentation of a region of interest;

FIG. 7 is a schematic view of a display window for displaying a volumerendered image, a surface rendered image, and three multiplanar imagesalong three different planes of a selected organ system;

FIG. 8 is flowchart representing a method for determining a central paththrough the lumen of a hollow organ;

FIG. 9 is a schematic view of a display window for displaying a volumerendered image, a surface rendered image, and three multiplanar imagesalong three different planes of a selected organ system combined in asingle window display; and

FIG. 10 a-h are schematic illustrations depicting a process forsplitting a colon.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention generally relates to a method and system, asschematically represented in FIGS. 1-4, for generating interactive,three-dimensional renderings of three-dimensional structures generallyhaving a lumen. The structures are usually in the general form ofselected regions of a body and in particular, human or animal bodyorgans which have hollow lumens such as colons, blood vessels, andairways. In accordance with the present invention, the interactivethree-dimensional renderings are generated in a computer-controlledprocess from a series of two-dimensional, cross-sectional images of theselected body organ acquired, for example, from a helical computedtomography (CT) Scan. The three-dimensional renderings are interactivein that such renderings can be manipulated by a user on a visual displayof a computer system, such as a computer monitor, to enable movement in,around and through the three-dimensional structure while simultaneouslydisplaying multiplanar views centered on a point that changes inresponse to the movement, or on a point selected by the user.

The computer-generated interactive three-dimensional renderings can beconstructed from surface renderings of the organ system or from volumerenderings of the organ system and surrounding anatomy and, often,presented simultaneously in the same three-dimensional display window.In a selected embodiment, a surface rendering of an organ can beincorporated into a volume rendering of the surrounding anatomy usingtexture memory in order to achieve a combined surface/volume rendering,or a hybrid rendering effect, wherein the surface rendered organ may behighlighted in the context of the volume-rendered surrounding anatomy.In yet another embodiment, multiplanar views such as orthogonal(sagittal, coronal, and axial) or oblique views, may be presentedsimultaneously with a surface-rendered organ and surroundingvolume-rendered anatomy in either separate three-dimensional displaywindows or in a combined single three-dimensional or display window.

A computer-controlled system 20 in accordance with the present inventionis shown schematically in FIG. 4. The system 20 is generally asdescribed in co-pending application Ser. No. 08/331,352, which isincorporated herein by reference. Briefly, a computer console 24 is usedto operate a scanner 22 to produce a series of two-dimensional,cross-sectional images of a selected three-dimensional object. Thetwo-dimensional images are transferred from the console 24 to a computergraphics workstation 26 over a computer network 25. The graphicscomputer 26 is used to generate a three-dimensional rendering, such as athree dimensional surface rendering, of the selected object and toautomatically identify potentially abnormal regions of the structure.The three-dimensional rendering is displayed on a computer monitor 28.Additionally, the displayed rendering can be recorded on a videorecorder 30 or photographed for future viewing. Various inputs, such asa computer mouse 27, are provided to the graphics computer to permit auser to manipulate the displayed imagery.

The scanner 22 can be a General Electric HiSpeed Advantage Helical CTScanner (GE Medical Systems, Milwaukee, Wis.). For scanning a patient'slungs, the scanner is typically operated with an X-ray beam collimationof 3 mm, a pitch of 1:1, a 1 mm reconstruction interval, a displayfield-of-view of 25.6 cm, and a 512×512 matrix. For scanning a patient'scolon, the scanner is typically operated with a beam collimation of 5mm, a pitch of 2:1, a 1 mm reconstruction interval, a displayfield-of-view of 40 cm, and a 512×512 matrix. These protocols typicallyresult in approximately 200 thoracic CT images or 500 abdominal CTimages, which occupy approximately 100 MB and 250 MB of disk storagespace, respectively. However, the protocols can be modified to optimallyacquire image data of specific organ systems.

The image data can be stored in any of a variety of image formats. Forexample, the image data can be stored in the digital imaging andcommunications in medicine (DICOM) standard, or as raw binary slices, orin a variety of volume formats. For example, the image data can bestored in the computer memory in an internal data format which allowsthe image files to be saved as a single data volume instead ofindividual image files. The internal data format can also permitstandard compression and uncompression techniques to be incorporated,thereby reducing computer disk storage requirements.

A method for generating interactive, three-dimensional renderings of aselected structure having a lumen and indicating potentially abnormalregions of the structure in accordance with the present invention isgenerally set forth in FIG. 1. The method can be implemented bycomputer. The steps of patient preparation 32, acquisition oftwo-dimensional images 33, formation of a three-dimensional volume 34,segmentation of a region of interest 35, creation of an isosurface ofthe region of interest 37, and generation of a wireframe model of theisosurface 38 may be effected in the general manner described inco-pending application Ser. No. 08/331,352, which is incorporated hereinby reference or any comparable methodologies and algorithms.

Patient preparation 32 will be dictated by the organ system to berendered. For example, if the patient's colon is to be rendered, thepatient is prepared using bowel cleansing and insufflation with gas todistend the colon. In addition, the patient can be given an oralcontrast agent to become incorporated in the fecal content to readilydistinguish the stool from surrounding soft tissue structures, includingthe colon wall. Without adequate bowel cleansing or use of an oralcontrast agent, stool and normal colon wall or colon lesions can beindistinguishable in computed tomography or other radiologic modalityimages. For example, the contrast agent may comprise a low-density(e.g., 1.5% w/v) barium-containing mixture. The opacified stool may thenbe digitally subtracted from the CT image data in order to render anunimpeded colon. Alternatively, for rendering a patient's airways, thepatient can be administered a bolus of non-ionic intravenous iodinatedcontrast agent to aid in distinguishing the blood vessels surroundingthe airways.

Once the patient has been prepared, two dimensional, cross-sectionalimages of the selected structure are acquired at step 33 with the use ofa scanner 22, such as a helical computed tomography (CT) scanner ormagnetic resonance imaging (NMR) scanner. The two-dimensional images arearranged in computer memory 21 (i.e., random access memory (RAM)) tocreate a three-dimensional data volume at step 34. To create isocubicvolume elements (i.e., voxels), an interpolation method, including butnot limited to trilinear interpolation, can be applied.

At step 35, a region of interest is segmented from the three-dimensionaldata volume. The purpose of segmentation is to isolate a region ofinterest within the three-dimensional data volume prior tothree-dimensional rendering. In general, medical image segmentation iscomplicated by image noise, partial volume effects, and the fact thatsimilar intensity values are shared by different anatomical structures.However, when a thin-walled soft tissue structure encompasses anair-filled lumen, segmentation of that structure can be effectuated byselecting the air column as the region of interest. The air column canbe effectively segmented because the boundary between air and softtissue is relatively distinct and sharp. Further, the outside surface ofthe air column corresponds to the inside surface of the organ ofinterest. Similarly, when a thin-walled soft tissue structureencompasses a contrast enhanced blood-filled lumen, segmentation of thatstructure can be effectuated by selecting the contrast-enhanced bloodcolumn as the region of interest. Accordingly, a simple thresholding orregion growing technique, along with a morphological dilation away fromair or blood vessel and towards soft tissue, can be utilized forsegmentation purposes.

However, in order to refine and to improve the accuracy of thesegmentation process, especially as selected structures have varyingcharacteristics (e.g., changing diameter, wall thickness, and/or X-rayattenuation values), more sophisticated segmentation procedures can beemployed. One such procedure adaptively adjusts the threshold valuedefining the region of interest to more accurately segment specificsections of the organ during a region growing process. For example,adaptive thresholding techniques can be used to segment a patient'sairways, especially as a threshold value of −425 Hounsfield units (HU)may be appropriate for the larger airways, but −800 HU may be moreappropriate for smaller airways. Therefore, by varying the thresholdvalue between these two extremes, the influence of airway structure andsurrounding tissues can be accounted for, especially as partial volumeeffects become apparent in the smaller airways.

A computer-executed method for adaptively adjusting threshold values forthe airways is shown in FIG. 6. At step 75, the region of interest issegmented using a three-dimensional region growing technique and aninitial static threshold value. The threshold value chosen shouldapproach the maximum threshold value which can be selected withouthaving the segmentation procedure fail by including surroundingstructures as part of the region of interest. For example, whensegmenting the air column of an airway, an initial threshold value of−800 HU may be appropriate. An initial wireframe model of the anatomy iscreated using a marching cubes variant.

The voxels or volume elements of the segmented region of interest aretagged at step 77 by setting a high-order bit in the 16-bit computerword used to store the voxel values (usually only the lower-order 12bits of a 16 bit word hold any actual data). At step 79, the taggedvoxels are used to find the boundary and voxel counts in two-dimensionsfor each airway segment on every image slice. The voxels within eachboundary are distinctly re-tagged at step 81 by setting a differenthigh-order bit. At step 83, the vertices on the initialthree-dimensional wireframe model closest to each two-dimensionalboundary point of each segment are found. An intensity profile alongeach vertex's normal vector is calculated to measure x-ray attenuationsfactors incremental along the normal directions at step 85. Theintensity profiles extend bidirectionally, into the surrounding tissueuntil the voxel values begin to decrease, and into the airway lumenuntil the voxel values begin to increase.

The adaptive threshold value is calculated for each segment on everyimage at step 87 as an average value of several measured thresholdvalues for each segment. Each adaptive threshold value can be calculatedas a variable percentage of the difference between the average maximumand minimum attenuation factors for the corresponding intensity profile.For example, a percentage of 50% corresponds to thefull-width-at-half-maximum measure of the intensity profile. Theadaptive threshold values are then used to re-segment the region ofinterest at step 88 using a region growing process that can account forthe varying threshold values.

Alternatively, the adaptive threshold values can be calculated bymorphing a ring which represents the intersection of the airway and aperpendicular plane along the skeleton of the airway. The skeleton is asequence of connected points that lie along the center of the aircolumns, like a central path. The initial reference ring lies in theinitial perpendicular plane and is the set of iso-value points of anunderestimated threshold value on that plane in the largest branch ofthe airway. Using this ring as a reference, the threshold value ischanged to create a new ring in the same plane, with a largercircumference and larger curvature values than the reference ring. Ifthe increase in circumference and curvature values are below a certainlimit, then the old threshold value is replaced by the new threshold,and the process is repeated. When the maximums are exceeded, the newthreshold value is stored on the skeleton, the perpendicular plane isshifted to the next point on the skeleton, and the previous thresholdvalue is used as the initial threshold value for the iso-value ring onthe new plane. This process is repeated for every branch along theskeleton until every point in the skeleton has an associated adaptivethreshold value. A variant of the marching cubes can then be used togenerate a variable-value surface using the variable threshold values.

Another method for adaptively adjusting the threshold valve used to growan object, uses the morphological skeleton of an approximate object tochoose optimal threshold values for all parts of the final object. Theobject is first grown using a lower threshold value. This gives the basestructure, but usually does not accurately segment all of the object.Then, the medial axis is calculated from the morphological skeleton ofthe object. For each voxel on the medial axis, the algorithm searchesout (in bi-directional rays perpendicular to the local axis) the localmaxima and minima of voxel values along the search line. Thefull-width-at-half-the-maximum value, or a variable percentage value, isthen calculated for each bi-directional ray, and all rays for aparticular axis point are averaged to give a threshold value at the axispoint. During segmentation, the nearest axis point to the current voxelis determined, and the threshold value associated with that point isused to classify the voxel as being inside or outside the object using aregion growing procedure.

Yet another technique for adaptive thresholding involves manuallyspecifying markers in the volume to hold threshold values that bestapproximate the object at that location. The markers are established forall sections of the object that need special threshold values. A globalthreshold value is also specified. During the region growingsegmentation process, the algorithm checks to see if the current voxelis near a threshold marker. If so, the threshold value associated withthe marker is used to classify the voxel as being inside or outside theobject. The range for calling a position near the marker, as well as thebehavior if the position is inside the specified range (the thresholdcan be linearly scaled or applied unchanged), can be user-specified.However, if the current voxel is not near a threshold marker, the globalthreshold is used to classify the voxel.

Still another technique for adaptive thresholding modifies the thresholdvalue as a function of the distance from the starting seed of the regiongrowing procedure. The user may specify that voxels near the seed shouldbe within a certain range, but voxels farther away from the seed shouldfall within a different range. The thresholds for positions in betweencan be found by interpolating between the ranges. For this technique,any number of distance and threshold ranges can be specified.

Returning to FIG. 1, once the region of interest has been segmented, anisosurface of the region of interest is created at step 37. Theisosurface can be generated using a variant of a marching cubealgorithm. The isosurface is then used to generate a wireframe model atstep 38. The wireframe model comprises a polygonal mesh that correspondsto the surface of the region of interest.

After the wireframe model has been generated at step 38, connectivitymatrices are determined at step 39. The connectivity matrices are datastructures which provide information regarding the connectivity betweenthe vertices and polygons which comprise the wireframe model. Theconnectivity matrices are determined by traversing the polygon andvertex lists associated with the wireframe model, generating sets ofimmediate neighbor vertices and triangles associated with each vertexand polygon in the lists. For example, the vertex to polygonconnectivity matrix contains, for each vertex in the wireframe model, alist of all polygons that contain that vertex. Likewise, a polygon topolygon matrix lists all adjacent polygons for each polygon in themodel.

Using these connectivity matrices, the polygon list of the wireframemodel is then re-ordered. The polygons generated using the variant ofmarching cubes are usually added to, and as such ordered in, the polygonlist as the three dimensional volume is traversed. Accordingly, all ofthe polygons between the first and second images are generated first,followed by the polygons between the second and third images, and soforth. This polygon ordering is not intuitive, nor ideal for analysis ormanipulation of the geometry at the local connectivity level.Consequently, to group vertices into populations and to analyze thepopulations, it is advantageous to re-order the polygons into thesequence by which they would be encountered while “traveling” throughthe lumen of the wireframe model. In addition, the re-orderingfacilitates rapid three-dimensional renderings, since hidden objects orobjects outside of the field-of-view can be easily identified andremoved, thereby reducing the number of polygons rendered and thusincreasing the rendering speed.

The re-ordered connectivity matrices are also a means to rapidlycalculate an arbitrarily oriented cross-sectional area of an object or aregion of interest. This is done by generating the intersection of thewireframe model with a plane of user-defined orientation. Threeuser-defined points of intersection with the object defines the plane.The intersection of the plane with the wireframe model is outlined by aset of points that are generally in the shape of a distorted ring. Byconnecting these N points, a polygon with N sides is formed. Thispolygon can be divided into N−2 triangles by connecting the first pointin the ring with every other point on the ring. The cross-sectional areacan be approximated by summing the areas of each triangle.

The vertices of the wireframe model can be analyzed and grouped intopopulations having abnormal wall structure as shown at steps 40-45 inFIG. 1. At step 40, a normal vector is calculated for each vertex in athe wireframe model. The direction of each normal vector isperpendicular to a plane that is tangent to the isosurface at each suchvertex, typically pointing away from the object or away from the lumenof a body organ. The normal vectors at the respective vertices can becomputed as the average of the normal vectors associated with eachpolygon connected to that vertex. The normal vector associated with apolygon is calculated using a vector cross product. Alternatively, thenormal vector at a vertex can be computed as a weighted average of thenormal vectors associated with those polygons of the wireframe modelwhich are within a predetermined distance from a specific vertex. Athird method is to compute the normal vector at a vertex, component-wise(e.g., x, y, and z), by calculating the three dimensional gradients ofthe local volume surrounding the vertex.

The wall thickness of the structure at each vertex is measured at step41. The wall thickness at a given vertex can be determined by measuringthe distance from the inner surface of the selected object to its outersurface or some other position between the inner and outer surfaces. Forbody parts such as the colon or airways, the selected structure's wallis composed of soft tissue and the lumen of the structure comprises air.Accordingly, the inner surface corresponds to an air/soft tissueinterface. Further, the colon and airways are typically surrounded byfat, air, or other non-soft tissue material (i.e., contrast-enhancedblood vessels, bone, or calcification). Accordingly, the outer surfacecorresponds to the soft tissue/air, soft tissue/bone, soft tissue/fat,or other such interface. The X-ray attenuation factors for air(approximately −1024 HU to −425 HU), fat (about −200 HU to −20 HU), andother non-soft tissue structures such as contrast-enhanced blood vesselsand bone (approximately >100 HU) are distinct from the soft tissuecomponent (about 20 HU to 100 HU) of the airways or colon wall.Accordingly, the wall thickness at a vertex on the wireframe model canbe calculated from the volume data of the selected organ, which can bemeasured at regularly spaced intervals along each normal vector, as willnow be described.

In general, for each vertex, v, and its corresponding normal vector, N,an X-ray attenuation factor, A(p_(i)), can be determined for each pointp_(i) along the normal, where p_(i)=v+iN for 1≦i≦max_depth and max_depthis a user-definable maximum search depth, typically set at 15. The wallthickness value, T, is initially set to zero but is incremented by onefor each consecutive point p_(i) which has a corresponding X-rayattenuation value that falls within a range of −425 HU to +100 HU andthat satisfies certain criteria.

Specifically, the wall thickness at each vertex can be measured as shownin FIG. 3. At step 56, one of the vertices, v, of the wireframe model isselected. The thickness, T, at that vertex is initially set to zero atstep 57 and a counter, i, is set to one at step 58.

The voxel value, such as the x-ray attenuation factor, A(p_(i)), for thei^(th) point along the normal is measured at step 59. At step 60, it isdetermined whether i=5. If i does not equal 5, then it is determined ifA(p_(i)) is between −425 HU and +100 HU, and if A(p_(i)) is between −425HU and +100 HU, then the thickness T is incremented by one at step 61.The process proceeds to step 65.

At step 65, it is determined whether A(p_(i))<−100 HU and A(p_(j))≧−100HU for some value of 1≦j≦(i−1). If this expression is satisfied, then asoft tissue/fat or soft tissue/air interface is considered to have beenreached. Accordingly, the thickness T is not incremented and the processproceeds to step 71.

At step 71, it is determined whether there is a vertex whose thicknesshas not yet been measured. If there is a vertex whose thickness has notbeen measured, that vertex is selected at step 56. Otherwise, the wallthickness at all vertices would have been measured and the process endsat step 72.

Returning to step 65, if the expression at step 65 is not satisfied,then it is determined at step 66 whether A(p_(i))>100 HU. If A(p_(i)) isgreater than 100 HU, then a soft tissue/bone interface or an interfacewith some other high-density material is considered to have beenreached. Accordingly, the thickness T is not incremented and the processproceeds to step 71.

If A(p_(i)) is not greater than 100 HU at step 66, then the processproceeds to step 68 where counter i is incremented by one. At step 70,it is determined if counter i exceeds the predetermined value ofmax_depth. If i>max_depth, then the process proceeds to step 71. If,however, i≦max_depth, then A(p_(i)) is measured at step 59.

Returning to step 60, if i=5, then the attenuation factors for the firstfive points, A(p₁) through A(p₅), are evaluated at step 62 to determineif all of those attenuation factors are less than −110 HU. If all ofthose attenuation factors are less than −110 HU, the wall thickness atthat vertex is considered to be normal. This situation often arisesalong the colon's haustra, or where opposing segments of gas distendedbowel touch. Accordingly, the thickness value, T, is set to zero at step63 and the process proceeds to step 71. By assigning T=0 to a vertex, v,that vertex is considered to be associated with normal colon wall.

Returning to step 62, if the attenuation value at any of the first fivepoints (A(p₁) through A(p₁)) exceeds −110 HU, then the wall thicknessdetermination continues at step 61.

Since the wall thickness of body structures such as the colon may beinadvertently or artificially increased, such as during musculaturecontraction of the colon at certain positions along the colon structure,many of the regions of abnormal thickness may not be indicative of truelesions. Instead, the thickened regions may represent normal structuralelements, such as regions where the colon is contracted or where ittouches solid organs. Accordingly, it can be beneficial to furtheranalyze and characterize each vertex associated with abnormal thicknessto refine the list of potentially abnormal vertices. Additional localshape features can be used to characterize and associate vertices asbeing abnormal.

For example, a local convexity value at each of the surface vertices canbe determined at step 42. The local convexity values can be determinedfor all vertices, or for only those vertices associated with abnormalwall thickness. For each vertex, its neighboring vertices within aconnected neighborhood of a pre-determined size (e.g., 3), is determinedusing the connectivity matrices. A local convexity value for eachvertex, v, is computed as:

$C = {\sum\limits_{i = 1}^{n}{D\left( v_{i} \right)}}$wherein D(v_(i)) is the distance from the i^(th) neighboring vertex to aplane through v and perpendicular to v's normal vector, and n is thenumber of neighboring vertices. Vertices associated with a convexityvalue of greater than a pre-defined maximum (e.g., >2) can be consideredabnormal.

In addition, the local curvature at each vertex in a population can bedetermined at step 43 in FIG. 1. The curvature reflects another shapefeature of the object's surface at a given vertex. Vertices associatedwith broad curvatures indicate regions that are relatively flat.Accordingly, if the user wishes to identify only regions havingthicknesses that vary significantly over short distances, those verticeswith small curvatures may be labeled as abnormal.

The local curvature at each vertex in a population can be determined bysumming the scalar distance between each of a user-determined number oflevels, N, of adjacent vertices and the plane perpendicular to thenormal of the vertex. The result is then normalized by the surface areaoutlined by the N h level of adjacent vertices. Because the direction ofthe curvature is ignored, the technique is able to detect areas of smallcurvature.

At step 44, vertices representing an abnormality are selected. Thosevertices meeting selected criteria can be identified as abnormalvertices. For example, vertices having abnormal thickness and/orconvexity and/or curvature criteria may be identified as abnormalvertices. In a specific application, the wall thickness values, localconvexity values, and local curvature values can be used, independentlyor in conjunction with each other, to either accept or reject verticesas being abnormal. The individual parameters can be combined using alogical AND operation.

At step 45, the vertices on the wireframe model associated with abnormalstructure (i.e., having a combination of associated abnormal wallthickness and/or abnormal shape, such as abnormal local convexity and/orabnormal local curvature) are grouped into populations. The re-orderedconnectivity matrices are used to determine if vertices associated withabnormal parameters are directly connected to other vertices that arealso associated with abnormal parameters. Accordingly, each formedpopulation represents a potential abnormal lesion. In addition, eachpopulation can be further analyzed and characterized by its size,dimensions, or other statistical quantities.

Generally, the size of a population is indicated by the number ofvertices which comprise the population. At step 46, those populationshaving a size below a predetermined minimal value are excluded frombeing considered abnormal. If the size of a population is sufficientlysmall, then the population is unlikely to represent a true lesion.Instead, the population more likely represents a normal aberration inthe structure or image segmentation process. Accordingly, elimination ofthose populations having a size below a minimum value, decreases theoccurrence of false positive findings.

Each population is further analyzed according to shape at step 47 toreduce the occurrence of false positive findings. Since the structure ofbody organs may appear abnormal, either inadvertently or artificially,such as during muscular contraction of the colon at certain positionsalong the colon's structure, many of the abnormal populations may not beindicative of true lesions. Instead, these populations may representnormal structural elements, such as regions where the colon iscontracted or where it touches solid organs. Accordingly, it may beadvantageous to further analyze and characterize each populationidentified as having an abnormal structure to further refine the list ofpotential lesions and to increase the likelihood that a detectedpopulation represents a true abnormality.

For example, the shapes of the populations can be analyzed,characterized, and accepted or rejected as being abnormal as illustratedin detail in FIG. 2. At step 120 a centroid for each population iscalculated. The centroid can be computed as a weighted sum of thepositions of each vertex within the population. The individual verticescan be weighted by their associated wall thickness values or otherparameters. Once the centroid has been determined for a population, theheight and normal vector for that population can be determined.

A population normal vector is calculated for each population at step121. The population normal vector can be computed as a sum of the normalvectors at each of the vertices within the population, and theindividual vertex normal vectors can be weighted by their correspondingwall thickness values or other parameters.

The convexity value, (C), for each population is determined at step 122.The convexity is a measure of the direction and magnitude of the shapeof the surface of the population. As shown in FIG. 5, the convexity of apopulation is computed as the sum of the distances 94 from vertices 95in the population to a plane 96, perpendicular to the population normalvector 97 passing through the centroid 98, according to the equation:

$C = {\sum\limits_{i = 1}^{n}{D\left( v_{i} \right)}}$wherein D(v₁) is the distance from the i^(th) vertex of the populationto the plane 96 and n is the number of vertices 95 within apredetermined space relative to the centroid. Since the normals at eachvertex are typically assigned to point away from the center of theobject, or the lumen of the object, a population which protrudes intothe lumen of the object would have a positive convexity value, whereas apopulation that projects away from the lumen would exhibit a negativeconvexity value. When the structure is a colon, populations with anegative convexity value are excluded from being considered abnormal,since abnormal colon masses tend to project into the lumen of the colonand would have a positive convexity value.

Further, the higher the magnitude of the convexity value, the greaterthe slope of the surface of the population. When the structure is acolon, only populations having a positive convexity value above aminimum value are reasonably expected to be potential lesions, sincecancerous colon masses are generally manifested by steeply slopedgrowths that protrude into the lumen of the colon.

The height (H) of each population is determined at step 123. The heightis calculated as the difference between the distance of the vertexfarthest from a plane 96, perpendicular to the population normal 97 andpassing through the centroid 98, and the distance of the vertex closestto that plane according to the equation:H=MAXD(v _(i))−MIND(v _(i)).The height provides an indication as to whether a population isreasonably expected to be a true lesion since populations with largeheights are more likely to represent abnormal masses.

Other shape features may be used to include or exclude populations inthe list of potential lesions. For example, if a population has a longaxis dimension that is greater than a multiple (e.g., 10 times) of theshort axis dimension, and the short axis dimension is, for example, lessthan 5 mm wide, then the population might be considered to be normal.Accordingly, the shape features discussed herein are included only byway of example, and are not intended to limit the scope of theinvention.

Referring again to FIG. 1, after the populations with abnormal wallstructure (i.e., wall thickness, convexity, or other shape feature) havebeen identified and characterized, populations that fail to meet certaincriteria are eliminated as potential lesions at step 48.

At step 49, those populations which are still considered to representpotential lesions are sorted and arranged in an electronic listing. Thelisting could be sorted according to such properties as size, convexity,curvature, height, mean wall thickness, standard deviation of wallthickness, other statistical quantities, or a combination of suchparameters (e.g., the product of the group convexity value, C, and thegroup height value, H). Each population in the listing could be linkedto a three-dimensional camera position that best illustrates it in thethree-dimensional display. Accordingly, the user could choose apopulation from the listing and thereby be presented with a view of theabnormal area as it appears in the three-dimensional rendering. Onemeans of linking the population to a three-dimensional rendering cameraposition is by providing the user with a three-dimensional camera viewof the population that is parallel to the population normal and centeredon the centroid of the population, and set at a chosen distance from thecentroid. In one embodiment, the user is presented with two views of theselected population, one from an external perspective and the other froman internal perspective (i.e., from within the lumen of the structure).

A three-dimensional rendering of the wireframe model is displayed atstep 50. In one embodiment, the user can navigate through athree-dimensional rendering of the wireframe model which is displayed onthe monitor 28. The detected abnormal populations are displayed with acontrasting color scheme to distinguish them from the remainder of therendered structure, so that the abnormal populations can be quickly andeasily identified by human visual inspection. Further, the color schemecan reflect variations in wall thickness, convexity, or curvature, orsome other parameter. For example, the normal portions of the renderedstructure can be displayed in shades of pink while the potentiallyabnormal populations are displayed in shades of blue. More specifically,vertices could be assigned colors ranging from dark blue (for verticeswith minimum abnormal thickness) to bright blue (for vertices withmaximum abnormal wall thickness) to provide a stark contrast to thepinkish color assigned to the normal portions of the rendered structure.

Instead of navigating through the rendered structure, a “split open”view of the three-dimensional rendered structure can be displayed,thereby allowing the user to view the interior surface of the structure,like a topological map, without visual obstruction. The splitting can beaccomplished using the re-ordered connectivity matrices, describedabove, and a central path to divide the wireframe model into severalsmaller segments, and then split each segment in half to expose theinterior surfaces of the segments. The use of the connectivity matricesallows for the splitting of the wireframe to be independent of thedegree of overall curvature of the central path of the wireframe model.This approach overcomes the mathematical restrictions inherent in othercommonly used cutting plane techniques, such as found in a singleinfinite cutting plane, spherical-shaped cutting plane, orcylindrically-shaped cutting plane, and allows for an arbitrary numberof segments to be split with only a single pass over the wireframe'spolygon listing.

The splitting is accomplished by first approximating a curved cuttingsurface with a sequence of finite, intersecting planes. These planescontain (i.e., pass through) the central path of the wireframe model.The central path can be calculated, as shown in FIG. 8, by firstgenerating points that are medially located within the colon or lumen.Second, the detected medial points are linked into connected centralpaths and the path that is most appropriate for colon visualization isselected by removing any accessory paths that run through small holescaused by anatomical variations or image segmentation artifacts. Thecentral path algorithm uses an object's three-dimensional skeleton tofind a path that lies along the center of its lumen. First, a user picksa starting and an ending point for the path. These may be chosen usingthe multiplanar image display and a computer mouse. After the startingand ending points are selected, the algorithm performs athree-dimensional topological thinning of the object's region-grownsegmented data. Thinning is like peeling an onion: the outer layers ofthe object are incrementally removed so that the overall topology(shape) is preserved. The resulting structure is a three-dimensionalskeleton of the object, which, when the points are connected, generallyconsists of one-dimensional curves and two-dimensional surfaces. Thethinning phase can be improved by using equivalence classes to classifyneighborhoods of voxels instead of searching for all possibleneighboring relationships.

This method of three-dimensional topological thinning is significant inthree aspects. First, the determination of connectivity is moreefficient because it is based on finding equivalence rather thansearching all possible neighboring relationships, as is used in mostconventional methods.

Second, the thinning process never examines background points more thanonce. Special data structures called queues are used to improve theefficiency of thinning. This method saves time compared to conventionalmethods by storing surface points (voxels on the outer surface of theregion-grown object that are candidates for removal) in queues, aftergoing through the original volume during a single pass. As thinningproceeds, newly exposed surface points are added to the queues whenevera surface point on the queue is removed. The thinning process stops whenthe queues become empty.

Third, the distance transform for the three-dimensional region-grownobject is then calculated. The distance transform of a point in theobject is the distance from this point to the nearest volume point notin the object. The distance transform is used to find the centers ofmaximally inscribed balls (CMB) in the object's lumen. A CMB can bethought of as the largest sphere that can fit into the lumen of theobject. The centers of the CMB's can be used as anchor points in thethinning process, which insures that the generated central path ismedially located and insensitive to the orientation of the originalobject (e.g., colon).

Many branches of medially located curves and surfaces will be generatedin the thinning process, so the user defined starting and ending pointsare used to remove dead-end branches. The result is a curve that startsand ends at the specified points but may contain loops that representaccessory passages through the object that go to the same place.

The path selection phase converts the skeleton points into a graphrepresentation by linking the points together into connected paths,which are called edges in the graph. The points where multiple pathsmeet are vertices in the graph. After the graph is built, unwanted edgesare removed based on the radii of the CMB's associated with the pointson the path, which is taken from the distance transform applied duringthe thinning phase.

The next step of path selection is basically a graph reduction process.A Greedy search algorithm is used for this purpose which removes alledges at each vertex except the two with the largest weights. The weightof each edge is a function of the radii of the points of the edge. Oneeffective weighting method finds the smallest CMB radius of all pointson the edge. Alternatively, the user may manually select the incorrectedges. After all incorrect edges have been removed, the remaining edgesform the central path along the lumen of the object, such as the centralpath of the colon.

Alternatively, the central path can be determined by selecting a firstseed point which lies within the lumen of the segmented object. Theplane passing through that point that has the minimum area of objectdissection is determined and the center of such area calculated. A newseed point is then selected which lies a pre-determined distance awayfrom the first seed point in a perpendicular direction relative to theplane of minimum area passing through the first seed point. A plane ofminimum area that passes through the new seed point is determined andthe center calculated. This iterative process is continued until acentral path connecting the center points is determined.

Referring to FIG. 10 a, a segment of colon 300 is schematically depictedhaving a center line 305. Now returning to splitting the object, byvariably subsampling points 310, 312, 314 along the central path, asshown in FIG. 10 b, a sequence of consecutive points, connected by linesegments 320, are determined. For each pair of these line segmentsconnected at a point, a slicing plane 325 perpendicular to the linesegment (a portion of the central path) and containing the point iscalculated. For each of these line segments, a halving plane 330 isdetermined, as shown in FIG. 10 c. The first halving plane is calculatedto contain the line segment 320 and a point 340, arbitrarily specifiedby an angle offset from a predetermined axis perpendicular to the linesegment. By changing the angle offset, as shown in FIG. 10 d, thehalving plane 330 is rotated around the line segment (like the hands ofa clock). The second halving plane 332 is calculated to contain thesecond line segment and the intersection of the first halving plane withthe first slicing plane. This second halving plane is unique and solelydependent on the orientation of the first halving plane. Accordingly,rotating the initial halving plane rotates the second halving plane.Similarly, the third halving plane 323 is calculated to contain thethird line segment, and the intersection of the second halving planewith the second slicing plane, and so forth. The resulting connectedhalving planes approximate a curved cutting surface that follows thecentral path, and the resulting slicing planes are used in conjunctionwith the re-ordered connectivity matrices to split along the halvingplanes.

The splitting of the wireframe model is, then, accomplished, as shown inFIG. 10 e, by selecting and marking an initial seed polygon 350. Usingthe re-ordered connectivity matrices, polygons adjacent to the seed arechecked against the first slicing plane 325, marking those that are onthe same side as the seed polygon. Each of the newly checked and markedpolygons becomes a new seed, and each of its unchecked and unmarkedpolygons are identically checked against the first slicing plane untilall polygons sequentially connected to the original seed polygon aremarked. As shown in FIG. 10 f, each of these marked polygons are thenchecked against the first halving plane 330 and stored, divided alongthe plane 330 to provide colon halves 360 and 361, as shown in FIG. 10g. An unmarked polygon adjacent to the last marked polygon is used as aseed for the second iteration of this process, as it is repeated untilno unmarked and unchecked polygons remain. As shown in FIG. 10 h, theresulting split and halved segments 360 and 361, 370 and 371, 380 and381, and 390 and 391, may be displayed individually or in groups, andcan be color enhanced to easily identify interior and exterior surfaces.

In an alternate embodiment, the user is presented with a volume rendereddisplay centered on the centroid of a selected abnormal population. Forexample, a volume-rendered view of a subvolume of user defined sizecentered on the centroid of the population can be displayed for visualinspection. Volume rendering is a visualization technique that creates athree-dimensional image using all of the volume data, rather than onlyan extracted surface representation. Traditionally, volume rendering isaccomplished by projecting a series of rays from the user's viewpointthrough the data volume. The color at each point (i.e., pixel) in theresulting three-dimensional image is determined by calculating aweighted sum of all voxels along each ray. This weighting factor isdetermined by a transfer function that is computed during a processcalled classification.

Altering the transfer function during volume rendering can revealdifferent characteristics of the rendered data and, thus, it is oftendesirable to interactively reclassify and re-render the data. However,the rate at which images are rendered can be slow for data volumes aslarge as the ones typically used in medical imaging. Accordingly, it canbe beneficial to use texture mapping to dramatically increase the rateat which volume rendering is performed. In texture mapping, volume datais considered to be a series of two-dimensional textures that areinterpolated, projected, and displayed rapidly on a series of polygonsusing specialized computer hardware.

The texture mapped volume rendering method uses high-speed texturememory and blending hardware available on a Silicon Graphics' computerworkstation to approximate the results of the more computationallyexpensive ray-casting algorithm. The volume data is first loaded intotexture memory. The textures are then semi-transparently mapped onto aseries of parallel two-dimensional rectangles that correspond to slicesof the volume. The texture mapped rectangles are then drawn and blendedto allow the user to see through the volume. The colors andtransparencies of the resulting three-dimensional object are controlledby color and opacity transfer functions. The entire volume can then beviewed by stacking the texture mapped rectangles in the proper order andlooking toward the stack. Since viewing the stack along one of its edgesdoes not optimally display the volume, a new stack is generated which isorthogonal to the original stack. This new stack is then used to displaythe volume along an edge. Likewise, for any additional viewing angles,additional stacks may be required. Generating the stacks is a dynamicprocess and each stack can be generated on an as needed basis. Texturemapping can use standard two-dimensional texture memory or fasterthree-dimensional texture memory.

In yet another alternate embodiment, upon selection of a population fromthe listing, the user can be presented with a three-dimensional,surface-rendered wireframe model of the population embedded within avolume-rendered display using texture memory techniques. Such a displayis useful to show the three-dimensional relationships between thewireframe model and the volume data. Embedding a surface-rendered modelinside a volume rendering can be accomplished using texture memory sinceboth techniques use semi-transparent or opaque surfaces to generatetheir imagery. However, if the surface rendering is semi-transparent,care must be taken to ensure that all objects are blended in the properorder (typically, from back to front). All or part of the volume can berendered. For example, to view a small structure using volume rendering,a subvolume of data surrounding that structure can be rendered, asopposed to displaying the entire volume of data. It should also beappreciated that the multiplanar views and volume rendering can becombined in a similar manner.

If the user wants to view the results of the various renderingtechniques using separate display windows, the user can clone thedisplay window 200 so that the two (or more) display windows share thesame data. However, each display window could render using differenttechniques (i.e., multiplanar display, surface rendering, volumerendering). Accordingly, the user could open a single window with allthree rendering techniques and then clone the window three times to vieweach technique separately. Changes made to the data in any one of theconnected windows would be propagated to all the cloned windows. Cameraorientations could be synchronized between the separate windows to lockthe views together. Position tracking techniques could show the cameraposition of one viewer in another, so that the user could follow thepath of a three-dimensional camera moving inside the structure in onedisplay window from an external perspective using a cloned displaywindow with another three-dimensional camera from an overhead position.

An example of a computer console display 100 useful for displaying athree-dimensional rendering of a structure is shown in FIG. 7. Thedisplay 100 comprises a first window 101 for displaying a volumerendered image of the structure and a second window 102 for displaying asurface rendered image of the structure. Alternatively, the secondwindow 102 can display a surface rendered image of the structureembedded within a volume rendered display, as described above. Inaddition, three multiplanar windows, 103, 104, and 105, are provided ina third window for displaying axial, coronal, sagittal, or obliquetwo-dimensional slices through the structure at a user-defined point, orat the centroid of a chosen population.

Alternatively a single window display 200, as shown in FIG. 9, can beused to display a three-dimensional rendering of the structure. Thesingle window display comprises a single three-dimensional displaywindow for presenting a combination of multiplanar images 203, 204 and205 with a volume-rendered image 207 centered on a point located withina surface-rendered image 209. The display presents a holistic view ofall the data that can aid in the visualization and understanding of theinter-relationships of the various rendering techniques (i.e., surfacerendering, volume rendering, and intersecting two-dimensional planes).The various rendering techniques can exist together because they are allbased on the same patient coordinate system supplied by the imagescanner, and they are all rendered using shared three-dimensionalgraphics techniques (texture memory) and a common graphics library. Theintersecting planes can be rendered as opaque textures (as opposed tosemi-transparent textures used in volume rendering) mapped ontotwo-dimensional rectangles using texture mapping and drawn in theirproper orientation. The user can interactively scroll through slicesparallel to any of the intersecting planes and can view any combinationof planes (e.g., axial and/or sagittal and/or coronal planes), or theuser can create and view an oblique slice plane. The planes can berendered together with the surface data to show the exact relationshipbetween the wireframe model and the volume (by viewing the intersectionsand orientations of the surface rendered wireframe model and thetwo-dimensional slices). They can be viewed together because the planesare rendered as any other kind of surface (i.e., a flat, two-dimensionalrectangle).

It will be recognized by those skilled in the art that changes ormodifications may be made to the above-described embodiments withoutdeparting from the broad inventive concepts of the invention. It shouldtherefore be understood that this invention is not limited to theparticular embodiments described herein, but is intended to include allchanges and modifications that are within the scope and spirit of theinvention as set forth in the claims.

1. A method for interactively displaying three-dimensional structurescomprising the steps of: a. forming a three-dimensional volume of datarepresenting at least one physical property associated with athree-dimensional body; b. isolating a selected region of interest bycomparing the value of each of a selected number of voxels in the volumeof data to a value range of selected values of the physical propertythat represent the selected region of interest to remove those voxelshaving a value outside the selected value range; and c. rendering theisolated region of interest in an interactive three-dimensional display,wherein the step of isolating the selected region of interest comprisesa morphological dilation step.
 2. The method as recited in claim 1wherein the body comprises a human body.
 3. The method as recited inclaim 1 wherein the forming step comprises an image acquisition stepwherein a series of two-dimensional images is acquired from which thethree-dimensional volume of data is formed.
 4. The method as recited inclaim 1 wherein the body comprises a colon.
 5. The method as recited inclaim 4 wherein the rendering step comprises simulating movement along aline which passes along the center of the lumen of the colon.
 6. Themethod as recited in claim 4 wherein the rendering step comprisessplitting the colon open in lengthwise sections and displaying the splitopen colon so that inner surfaces of the split open colon are visible.7. The method as recited in claim 6 wherein the splitting step includesthe steps of: a. defining a cutting plane through the isolated region ofinterest; and b. rendering all portions of the three-dimensional displayon one side of the cutting plane transparent.
 8. The method as recitedin claim 4 wherein the isolation step comprises a step of determiningcolon wall thickness.
 9. The method as recited in claim 8 wherein thestep of determining the colon wall thickness comprises displaying across-sectional image through the colon.
 10. The method as recited inclaim 8 wherein the step of determining colon wall thickness comprises astep of identifying a thickened section of the colon by visuallydifferentiating a thickened section on a displayed image from normalthickness of the colon wall.
 11. A method for interactively displayingthree-dimensional structures comprising the steps of: a. forming athree-dimensional volume of data representing at least one physicalproperty associated with a three-dimensional body; b. isolating aselected region of interest by comparing the value of each of a selectednumber of voxels in the volume of data to a value range of selectedvalues of the physical property that represent the selected region ofinterest to remove those voxels having a value outside the selectedvalue range; and c. rendering the isolated region of interest in aninteractive three-dimensional display wherein the body comprises atracheobronchial airway, a portion of the tracheobronchial airway isrendered transparent, and a portion of the tracheobronchial airway isrendered semi-transparent.
 12. The method as recited in any one of claim1, 4, or 11 wherein the three-dimensional volume of data comprises x-rayimages.
 13. The method as recited in any one of claim 1, 4, or 11wherein the step of isolating the selected region of interest comprisesa thresholding step for determining threshold values corresponding tothe selected values of the physical property to isolate the selectedregion of interest.
 14. The method recited in claim 13 wherein thethresholding step comprises: a. a threshold selection step for selectinga threshold range corresponding to the selected values of the physicalproperty representing the selected region of interest; and b. athreshold adjustment step for adjusting the threshold values to providethe threshold range for isolating the selected region of interest. 15.The method as recited in claim 14 wherein the threshold adjustment stepcomprises: a. an orthoslice step of providing an orthoslice through thevolume of data; and b. a display step for displaying the orthoslice anda corresponding thresholded image of the orthoslice, so that thethreshold values can be adjusted while comparing the thresholded imageto the corresponding orthoslice.
 16. The method as recited in claim 13wherein the wherein the morphological dilation step comprises the stepsof: a. undersegmenting the selected region of interest from the volumeof data; and b. adding a layer of voxels to the undersegmented region ofinterest to form a segmented region of interest.
 17. The method asrecited in any one of claim 1, 4, or 11 wherein the region of interestcomprises at least one of a bone-tissue interface, a bone-air interface,and an air-tissue interface.
 18. The method as recited in any one ofclaim 1, 4, or 11 wherein the three-dimensional volume of data comprisesimages taken at regularly spaced grid locations within the body.
 19. Themethod as recited in claim 18 wherein the spacing between successivegrid locations is selected to produce isocubic voxels forthree-dimensional display.
 20. The method as recited in any one of claim1, 4, or 11 wherein the physical property includes x-ray attenuation.21. The method as recited in any one of claim 1, 4, or 11 wherein thethree-dimensional volume of data comprises computed tomography images.22. The method as recited in any one of claim 1, 4, or 11 wherein theregion of interest comprises an air column.
 23. The method as recited inany one of claim 1, 4, or 11 wherein the isolation step comprises a stepof segmenting the selected region of interest.
 24. The method as recitedin any one of claim 1, 4, or 11 wherein the step of isolating theselected region of interest comprises a region growing step.
 25. Themethod as recited in any one of claim 1, 4, or 11 wherein the renderingstep comprises a volume rendering step.
 26. The method as recited in anyone of claim 1, 4, or 11 wherein the rendering step comprises a surfacerendering step.
 27. The method as recited in any one of claim 1, 4, or11 the isolated region of interest is rendered as a surface rendering inan interactive three-dimensional display, therein producing a virtualthree-dimensional environment, and wherein the rendering step comprisesa volume rendering step for rendering selected volumes adjacent theisolated region of interest.
 28. A method for interactively displayingthree-dimensional structures comprising the steps of: a. forming athree-dimensional volume of data representing at least one physicalproperty associated with a three-dimensional body; b. isolating aselected region of interest by comparing the value of each of a selectednumber of voxels in the volume of data to a value range of selectedvalues of the physical property that represent the selected region ofinterest to remove those voxels having a value outside the selectedvalue range; c. rendering the isolated region of interest in aninteractive three-dimensional display; d. forming an isosurfacewireframe model of the isolated selected region of interest wherein thestep of rendering an image includes the step of rendering athree-dimensional display of the wireframe model comprising a volumerendering step, and wherein the volume rendering step comprises anopacity adjusting step for adjusting opacity values.
 29. The method asrecited in claim 28 wherein the opacity adjustment step comprises thestep of adjusting the opacity values to the values determined along theinverse of a histogram curve representing the number of voxels of agiven voxel value as a function of voxel value.
 30. A method forinteractively displaying three-dimensional structures comprising thesteps of: a. forming a three-dimensional volume of data representing atleast one physical property associated with a three-dimensional body; b.isolating a selected region of interest by comparing the value of eachof a selected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; and c. rendering the isolated region of interestin an interactive three-dimensional display comprising a data reductionstep for reducing the size of the volume of data before rendering theisolated region of interest.
 31. The method as recited in claim 30wherein the reduction step comprises reducing pixel resolution.
 32. Themethod as recited in claim 30 wherein the reduction step comprisesreducing spatial resolution.
 33. The method as recited in claim 30wherein the reduction step comprises creating a subvolume of data.
 34. Asystem for interactively displaying three-dimensional structurescomprising: a. volume formation means for forming a three-dimensionalvolume of data representing at least one physical property associatedwith a three-dimensional body; b. isolation means for isolating a regionof interest by comparing the value of each of a selected number ofvoxels in the volume of data to a value range of selected values of thephysical property that represent the selected region of interest toremove those voxels having a value outside the selected value range, theisolation means comprising a morphological dilation means for adjustingthe isolated region of interest; and c. rendering means for renderingthe isolated region of interest in an interactive three-dimensionaldisplay.
 35. The system as recited in claim 34 wherein the isolationmeans comprises a thresholding means for determining a threshold rangeused to isolate the region of interest.
 36. The system as recited ineither of claim 35 or 34 including means for producing a wireframe modelof the isolated region of interest.
 37. The system recited in either ofclaim 35 or 34 wherein the thresholding means comprises: a. a thresholdselection means for selecting threshold values; and b. a thresholdadjustment means for adjusting the threshold values.
 38. The system asrecited in claim 37 wherein the threshold adjustment means comprises: a.orthoslice means for taking an orthoslice through the region of interestto produce an orthoslice image; and b. display means for displaying theorthoslice image and a corresponding thresholded image, so that thethreshold values can be adjusted while comparing the thresholded imageto the corresponding orthoslice.
 39. The system as recited in claim 34wherein the isolation means comprises a segmentation means forsegmenting a region of interest from the volume of data.
 40. The systemas recited in claim 34 wherein the rendering means comprises simulationmeans for simulating movement along a center line through the isolatedregion of interest of the three-dimensional display.
 41. The system asrecited in claim 34 wherein the rendering means comprises a volumerendering means.
 42. The system as recited in either claim 34 or claim41 wherein the rendering means comprises a surface rendering means. 43.The system as recited in claim 34 wherein the rendering means comprisesa surface rendering means for rendering the isolated region of interestas a surface rendering and wherein the rendering means comprises avolume rendering means for rendering selected volumes adjacent theisolated region of interest.
 44. The system as recited in claim 34wherein the morphological dilation means comprises: a. undersegmentationmeans for undersegmenting the region of interest from the volume ofdata; and b. layer means for adding a layer of voxels to theundersegmented region of interest to form an isolated region ofinterest.
 45. A system for interactively displaying three-dimensionalstructures comprising: a. volume formation means for forming athree-dimensional volume of data representing at least one physicalproperty associated with a three-dimensional body; b. isolation meansfor isolating a region of interest by comparing the value of each of aselected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; c. rendering means for rendering the isolatedregion of interest in an interactive three-dimensional display; and d.reduction means for reducing the size of the volume of data.
 46. Asystem for interactively displaying three-dimensional structurescomprising: a. volume formation means for forming a three-dimensionalvolume of data representing at least one physical property associatedwith a three-dimensional body; b. isolation means for isolating a regionof interest by comparing the value of each of a selected number ofvoxels in the volume of data to a value range of selected values of thephysical property that represent the selected region of interest toremove those voxels having a value outside the selected value range; andc. rendering means for rendering the isolated region of interest in aninteractive three-dimensional display, wherein the rendering meansincludes transparency means for rendering a portion of the region ofinterest semi-transparent.
 47. A system for interactively displayingthree-dimensional structures comprising: a. volume formation means forforming a three-dimensional volume of data representing at least onephysical property associated with a three-dimensional body; b. isolationmeans for isolating a region of interest by comparing the value of eachof a selected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; and c. rendering means for rendering the isolatedregion of interest in an interactive three-dimensional display, whereinthe rendering means includes splitting means for splitting thethree-dimensional display open in selected split open sections anddisplaying means for displaying the split open sections so that innersurfaces of the split three-dimensional display are visible, and whereinthe splitting means includes center line splitting means for splittingopen the three-dimensional display of the isolated region of interestalong a line which passes along a center of the three-dimensionaldisplay of the segmented region of interest, the center line splittingmeans including: a. selection means for selecting a seed point whichlies within the region of interest; b. plane determining means fordetermining a plane of minimum area through the region of interest thatpasses through the seed point; c. center point determining means fordetermining the center point of the region of interest that is dissectedby the plane of minimum area; and d. point selecting means for selectinga new point which is spaced from the previous center point in aperpendicular direction relative to the plane of minimum area.
 48. Asystem for interactively displaying three-dimensional structurescomprising: a. volume formation means for forming a three-dimensionalvolume of data representing at least one physical property associatedwith a three-dimensional body; b. isolation means for isolating a regionof interest by comparing the value of each of a selected number ofvoxels in the volume of data to a value range of selected values of thephysical property that represent the selected region of interest toremove those voxels having a value outside the selected value range; andc. rendering means for rendering the isolated region of interest in aninteractive three-dimensional display, wherein the rendering meansincludes splitting means for splitting the three-dimensional displayopen in selected split open sections and displaying means for displayingthe split open sections so that inner surfaces of the splitthree-dimensional display are visible, and wherein the splitting meansincludes center line splitting means for splitting open thethree-dimensional display of the isolated region of interest along aline which passes along a center of the three-dimensional display of thesegmented region of interest, the center line splitting means including:a. erosion means for iteratively eroding the region of interest untilall of the region of interest disappears, thereby determining the lastportions of the region of interest to disappear; and b. connection meansfor connecting the last portions of the region of interest to disappearby erosion.
 49. A system for interactively displaying three-dimensionalstructures comprising: a. volume formation means for forming athree-dimensional volume of data representing at least one physicalproperty associated with a three-dimensional body; b. isolation meansfor isolating a region of interest by comparing the value of each of aselected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; and c. rendering means for rendering the isolatedregion of interest in an interactive three-dimensional display, whereinthe rendering means comprises simulation means for simulating movementalong a center line through the isolated region of interest of thethree-dimensional display and wherein the simulation means includes: a.selection means for selecting a seed point which lies within the regionof interest; b. plane determining means for determining a plane ofminimum area through the region of interest that passes through the seedpoint; c. center point determining means for determining the centerpoint of the region of interest that is dissected by the plane ofminimum area; and d. point selecting means for selecting a new pointwhich is spaced from the previous center point in a perpendiculardirection relative to the plane of minimum area.
 50. A system forinteractively displaying three-dimensional structures comprising: a.volume formation means for forming a three-dimensional volume of datarepresenting at least one physical property associated with athree-dimensional body; b. isolation means for isolating a region ofinterest by comparing the value of each of a selected number of voxelsin the volume of data to a value range of selected values of thephysical property that represent the selected region of interest toremove those voxels having a value outside the selected value range; andc. rendering means for rendering the isolated region of interest in aninteractive three-dimensional display, wherein the rendering meanscomprises simulation means for simulating movement along a center linethrough the isolated region of interest of the three-dimensional displayand wherein the simulation means includes: a. erosion means foriteratively eroding the region of interest until all of the region ofinterest disappears, thereby determining the last portions of the regionof interest to disappear; and b. connection means for connecting thelast portions of the region of interest to disappear by erosion.
 51. Amethod for interactively displaying three-dimensional structurescomprising the steps of: a. forming a three-dimensional volume of datarepresenting at least one physical property associated with athree-dimensional body; b. isolating a selected region of interest bycomparing the value of each of a selected number of voxels in the volumeof data to a value range of selected values of the physical propertythat represent the selected region of interest to remove those voxelshaving a value outside the selected value range; and c. rendering theisolated region of interest in an interactive three-dimensional display,wherein the body comprises a colon and the rendering step comprisessimulating movement along a line which passes along the center of thelumen of the colon, and wherein the step of simulating movement alongthe center line through the isolated region of interest of thethree-dimensional display includes the steps of: a. selecting a seedpoint which lies within the region of interest; b. determining a planeof minimum area through the region of interest that passes through theseed point; c. determining the center point of the region of interestthat is dissected by the plane of minimum area; and d. selecting a pointwhich is spaced from the center point in a perpendicular directionrelative to the plane of minimum area.
 52. A method for interactivelydisplaying three-dimensional structures comprising the steps of: a.forming a three-dimensional volume of data representing at least onephysical property associated with a three-dimensional body; b. isolatinga selected region of interest by comparing the value of each of aselected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; and c. rendering the isolated region of interestin an interactive three-dimensional display, wherein the body comprisesa colon and the rendering step comprises simulating movement along aline which passes along the center of the lumen of the colon, andwherein the step of simulating movement along the center line includesthe steps of: a. iteratively eroding the region of interest until all ofthe region of interest disappears, thereby determining the last portionsof the region of interest to disappear; and b. connecting the lastportions to disappear by erosion.
 53. A method for interactivelydisplaying three-dimensional structures comprising the steps of: a.forming a three-dimensional volume of data representing at least onephysical property associated with a three-dimensional body; b. isolatinga selected region of interest by comparing the value of each of aselected number of voxels in the volume of data to a value range ofselected values of the physical property that represent the selectedregion of interest to remove those voxels having a value outside theselected value range; and c. rendering the isolated region of interestin an interactive three-dimensional display, wherein the body comprisesa colon, and the rendering step comprises simulating movement along aline which passes along the center of the lumen of the colon andsplitting the colon open in lengthwise sections and displaying the splitopen colon so that inner surfaces of the split open colon are visible,and wherein the rendering step comprises generating the split opensections along a line which passes along the center of the coloncomprising the steps of: a. selecting a seed point which lies within theregion of interest; b. determining a plane of minimum area through theregion of interest that passes through the seed point; c. determiningthe center point of the region of interest that is dissected by theplane of minimum area; and d. selecting a point which is spaced from thecenter point in a perpendicular direction relative to the plane ofminimum area.
 54. A method for interactively displayingthree-dimensional structures comprising the steps of: a. forming athree-dimensional volume of data representing at least one physicalproperty associated with a three-dimensional body; b. isolating aselected region of interest by comparing the value of each of a selectednumber of voxels in the volume of data to a value range of selectedvalues of the physical property that represent the selected region ofinterest to remove those voxels having a value outside the selectedvalue range; and c. rendering the isolated region of interest in aninteractive three-dimensional display, wherein the body comprises acolon, and the rendering step comprises simulating movement along a linewhich passes along the center of the lumen of the colon and splittingthe colon open in lengthwise sections and displaying the split opencolon so that inner surfaces of the split open colon are visible, andwherein the rendering step comprises generating the split open sectionsalong a line which passes along the center of the colon comprising thesteps of: a. iteratively eroding the region of interest until all of theregion of interest disappears, thereby determining the last portion ofthe region of interest to disappear; and b. connecting the last portionof the region of interest to disappear by erosion.